A template fusion method was proposed to improve ECG biometric recognition performance across varying physiological conditions including body posture, physical activity, and time lapse.
This study highlights the impact of physiological factors like body posture and physical activity on ECG biometric systems and proposes a template fusion approach to enhance recognition accuracy.
This paper addresses the challenges of evaluating electrocardiogram (ECG) biometric recognition systems. While this new biometric modality has attracted significant interest, a majority of the prior art has approached it from the perspective of a typical biometric modality and has neglected the physiological factors that directly affect the behavior of the respective systems. In an effort to bring to the table the idiosyncratic properties of the ECG biometric modality, this paper presents the UofT ECG database and offers a comprehensive analysis of the underlying interindividual variability under a number of conditions, such as body posture, physical activity, and time lapse. The performance of various methodologies is reported under the above-mentioned conditions and a method based on template fusion is proposed to address these shortcomings.
Wahabi et al. (2014) studied ECG biometric recognition. Template fusion method vs. Various methodologies was evaluated on Performance under varying physiological conditions (body posture, physical activity, time lapse). A template fusion method was proposed to improve ECG biometric recognition performance across varying physiological conditions including body posture, physical activity, and time lapse.